5 AI innovations in healthcare shaping diagnostics and drug development

Over 500 million global diabetes patients urgently need better wound care. The existing AI healthcare systems are unable to meet this demand.

AI healthcare systems struggle to detect chronic conditions accurately. They cannot reliably identify biomarkers from large medical datasets, leading to suboptimal patient care. Many innovators have identified this gap and are working towards commercial solutions using non-standard AI. 

This article highlights five AI healthcare advancements that enhance disease prediction and diagnostic accuracy. It explores new wound assessment systems, biomarker detection technologies, virtual cell simulation, and more. These breakthroughs offer significant potential to improve diagnostic accuracy, treatment personalization, and early disease detection.

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Adiuvo Diagnostics developed a conversational AI system to evaluate wound severity.

Market research shows traditional wound diagnosis methods are only 57% accurate and often uncomfortable for patients. Adiuvo Diagnostics’s wound assessment system improves this situation through personalized treatment recommendations.

It initiates patients using conversational AI to gather comprehensive medical information. This information includes disease history, family details, symptoms, and ongoing medications. Concurrently, an image analytics subsystem examines images of the wound. It categorizes them and assesses severity & risk through machine learning algorithms.

The system provides prognostics and personalized treatment recommendations. These include medications, dietary adjustments, lifestyle changes, antibiotics, and other therapies. Its explainable AI framework makes it easy for healthcare professionals and patients to understand the rationale behind treatment recommendations.

Stryker created Advanced Spine Guidance Software for Surgeons.

MedTech company Stryker has gained FDA approval for its innovative Q Guidance System. It includes the Spine Guidance 5 Software with a unique feature called Copilot. This system was developed with the input of over 850 spine surgeons and neurosurgeons.

This technology helps surgeons perform different steps like cutting bone, preparing spinal pedicles, and placing screws. It gives feedback through sound and touch, alerting surgeons when they get close to sensitive areas during surgery.

Over 2,400 surgical cases were completed using the Q Guidance System with Spine Guidance Software with positive feedback from surgeons. This highlights its practical design and effectiveness in improving patient outcomes.

Seoul National University’s AI system can detect vital biomarkers from vast medical data.

Seoul National University is developing an artificial intelligence-based device that identifies potential biomarkers in a patient’s records and medical history. The device scans data from sources such as electrocardiography (ECG), electroencephalogram (EEG), electromyogram (EMG), and medical images to find these biomarkers.

The AI system recognizes patterns in data, combining information from various sources to enhance biomarker identification. The results are helpful for new drug development, diagnosis, and treatment optimization across multiple diseases. This is especially beneficial for cardiovascular diseases, which lack effective biomarkers.

Accenture’s Investment in Turbine AI Advances Drug Discovery

Turbine AI, a simulation-based drug discovery company, has recently received an investment from Accenture. This support will help Turbine expand its work and reach more global pharmaceutical companies. Turbine’s Simulated Cell™ platform allows scientists to perform experiments on a computer that is difficult or impossible in a real lab. 

AI workflows like Turbine AI save up to 40% of the time it takes to bring a new molecule to the preclinical candidate stage. They also save 30% on costs for such a molecule until the preclinical candidate stage. The platform can experiments significantly faster than traditional wet-lab methods.

The investment from Accenture highlights the importance of Turbine’s work and will help the company grow its technology further. Turbine can enhance its platform and ensure that each experiment is accurate and effective in finding new treatments. This is especially helpful for diseases that are complex and hard to study in traditional ways.

Recommended: 5 AI Drug Development Startups to Watch in 2024 and beyond.

Waycen’s Machine Learning System Analyzes Social Anxiety Disorder

Waycen is a medtech company specializing in AI-based solutions. Their novel device analyzes social anxiety disorder using digital data. The device gathers information from different devices to identify patterns in people with social anxiety. It uses machine learning to differentiate between normal behaviors and those linked to the disorder.

This system uses real-time data from everyday device usage to identify subtle signs of social anxiety disorder. Traditional clinical settings would otherwise miss these symptoms. 

Future scope in Disease treatment and prediction by AI

AI in medical imaging is growing fast, expected to rise from USD 1 billion in 2023 to USD 14.8 billion by 2032, mainly due to advancements in deep learning, which holds 58.8% of the market. Hospitals using these technologies will change diagnostics, improve patient outcomes, and lower healthcare costs, particularly in North America and Asia-Pacific.

R&D teams must build complete platforms instead of single-point solutions. Their key priorities in 2025 will include biomarker detection, drug discovery simulation, and surgical guidance systems. Successful solutions require partnerships with specialized startups and clear frameworks for measuring clinical outcomes and ROI.

Searching for companies developing innovative AI tools for disease treatment and prediction will introduce many challenges, such as:

• Identifying relevant startups among hundreds of rapidly emerging names
• Difficulties in identifying the novel inventions among marketing hype
• Keeping up with the latest advancements in AI for disease management while managing existing research initiatives

And more! Partner with a research expert to find the right technologies for your goals. Fill out the simple form below to schedule a quick consultation with GreyB’s experts.

Authored by: Suborna Chatterjee, Patent Analytics

Edited by: Hemanth Shenoy, Market Research